In the field of magnetic resonance imaging (MRI), attempts have been made to use diffusion-weighted steady-state free precession (DW-SSFP) to estimate the apparent diffusion coefficient (ADC). See, for example, McNab J A, Miller K L. NMR Biomed. 2010; 23(7):781-93. Unfortunately, measuring diffusion using DW-SSFP is highly dependent on the relaxation parameters (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation), which therefore requires additional acquisitions and processing to quantify T1 and T2. Conventional magnetic resonance (MR) pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time (TE) while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation). Conventional MR pulse sequences may not recover of a free induction decay (FID).
When MR images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration. Thus, the images are only as good as the image interpreter and all image based (e.g., qualitative) diagnoses end up being subjective. Thus, techniques like DW-SSFP have attempted to quantify some MR information (e.g., apparent diffusion co-efficient).
Magnetic resonance fingerprinting (MRF) employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the RF is applied. The term “resonant species”, as used herein, refers to an item (e.g., water, fat, tissue, material) that can be made to resonate using NMR. By way of illustration, when RF energy is applied to a volume that has bone and muscle tissue, then both the bone and muscle tissue will produce an NMR signal. However the “bone signal” and the “muscle signal” will be different and can be distinguished using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions or previously acquired evolutions. A large set of known evolutions may be stored in a dictionary.
Characterizing the resonant species can include identifying different properties of a resonant species (e.g., T1, T2, diffusion resonant frequency, diffusion co-efficient, spin density, proton density). Additionally, other properties including, but not limited to, tissue types, materials, and super-position of attributes can be identified. These properties may be identified simultaneously using MRF, which is described in U.S. patent application “Nuclear Magnetic Resonance (NMR) Fingerprinting”, application Ser. No. 13/051,044, and in Magnetic Resonance Fingerprinting, Ma et al., Nature 495, 187-192 (14 Mar. 2013), the contents of both of which are incorporated herein by reference.